ESTIMATION OF THE ATOMIC RADII OF PERIODIC ELEMENTS USING SUPPORT VECTOR MACHINE

T.O. Owolabi 
Research Assistant/ Physics Department King Fahd University of Petroleum and Minerals,
Saudi Arabia
owolabitaoreedolakunle@gmail.com

K.O.Akande 

Graduate Student/Electrical Engineering Department King Fahd University of Petroleum and Minerals,
Saudi Arabia
koakande@gmail.com

S.O.Olatunji 
Assistant Professor Computer Science Department University of Dammam,
Saudi Arabia
Oluolatunji.aadam@gmail.com


Abstract 

Atomic radii of elements are experimentally obtained from crystallographic data. However, this is not a feasible approach for some elements with limited number of atoms in existence since radii could not be easily drawn from several types of bound in ionic, covalent and metallic crystals. Hence, this work employs artificial intelligence approach using support vector machine to accurately predict and estimate the atomic radii of elements in the periodic table in order to pave way for predicting atomic radii of elements that could not be easily determined from crystallographic data. We obtained an accuracy of over 99% on the basis of the correlation between the experimental and our predicted radii. The simplicity and accuracy of this approach depict an excellent measure of its tendency to predict atomic radii of any element whose atomic number is known.

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https://www.academia.edu/8209835/ESTIMATION_OF_THE_ATOMIC_RADII_OF_PERIODIC_ELEMENTS_USING_SUPPORT_VECTOR_MACHINE